Adaptive Learning of Polynomial Networks Genetic
Programming, Backpropagation and Bayesian Methods
N. Nikolaev, and H. Iba. Genetic and Evolutionary Computation Springer, (2006)June.
Abstract
This book delivers theoretical and practical knowledge
for developing algorithms that infer linear and
non-linear multivariate models, providing a methodology
for inductive learning of polynomial neural network
models (PNN) from data. The text emphasises an
organised model identification process by which to
discover models that generalise and predict well. The
empirical investigations detailed here demonstrate that
PNN models evolved by genetic programming and improved
by backpropagation are successful when solving
real-world tasks.
Adaptive Learning of Polynomial Networks is a vital
reference for researchers and practitioners in the
fields of evolutionary computation, artificial neural
networks and Bayesian inference, and for advanced-level
students of genetic programming. Readers will
strengthen their skills in creating efficient model
representations and learning operators that efficiently
sample the search space, and in navigating the search
process through the design of objective fitness
functions.
%0 Book
%1 nikolaev:2006:book
%A Nikolaev, Nikolay
%A Iba, Hitoshi
%B Genetic and Evolutionary Computation
%D 2006
%I Springer
%K Bayesian adaptive algorithms, artificial backpropagation, computation, evolutionary genetic inference, intelligence, learning, networks, polynomial prediction programming, time-series
%N 4
%T Adaptive Learning of Polynomial Networks Genetic
Programming, Backpropagation and Bayesian Methods
%X This book delivers theoretical and practical knowledge
for developing algorithms that infer linear and
non-linear multivariate models, providing a methodology
for inductive learning of polynomial neural network
models (PNN) from data. The text emphasises an
organised model identification process by which to
discover models that generalise and predict well. The
empirical investigations detailed here demonstrate that
PNN models evolved by genetic programming and improved
by backpropagation are successful when solving
real-world tasks.
Adaptive Learning of Polynomial Networks is a vital
reference for researchers and practitioners in the
fields of evolutionary computation, artificial neural
networks and Bayesian inference, and for advanced-level
students of genetic programming. Readers will
strengthen their skills in creating efficient model
representations and learning operators that efficiently
sample the search space, and in navigating the search
process through the design of objective fitness
functions.
%@ 0-387-31239-0
@book{nikolaev:2006:book,
abstract = {This book delivers theoretical and practical knowledge
for developing algorithms that infer linear and
non-linear multivariate models, providing a methodology
for inductive learning of polynomial neural network
models (PNN) from data. The text emphasises an
organised model identification process by which to
discover models that generalise and predict well. The
empirical investigations detailed here demonstrate that
PNN models evolved by genetic programming and improved
by backpropagation are successful when solving
real-world tasks.
Adaptive Learning of Polynomial Networks is a vital
reference for researchers and practitioners in the
fields of evolutionary computation, artificial neural
networks and Bayesian inference, and for advanced-level
students of genetic programming. Readers will
strengthen their skills in creating efficient model
representations and learning operators that efficiently
sample the search space, and in navigating the search
process through the design of objective fitness
functions.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Nikolaev, Nikolay and Iba, Hitoshi},
biburl = {https://www.bibsonomy.org/bibtex/29bba65a708980f496e7962cf9fd9153c/brazovayeye},
interhash = {1242001f88cfcfed25f915645109b80c},
intrahash = {9bba65a708980f496e7962cf9fd9153c},
isbn = {0-387-31239-0},
keywords = {Bayesian adaptive algorithms, artificial backpropagation, computation, evolutionary genetic inference, intelligence, learning, networks, polynomial prediction programming, time-series},
note = {June},
number = 4,
publisher = {Springer},
series = {Genetic and Evolutionary Computation},
size = {XIV, 316 pages},
timestamp = {2008-06-19T17:48:26.000+0200},
title = {Adaptive Learning of Polynomial Networks Genetic
Programming, Backpropagation and Bayesian Methods},
year = 2006
}